Search Results (61)

This article describes a field- and laboratory-based framework that can be used to monitor the C balance in Mediterranean pine forest ecosystems under different management practices that determine their structure and function. By jointly monitoring stand structure, gas exchange, litter, and decomposition dynamics, this protocol enables the assessment of how management-driven changes regulate carbon uptake, turnover, and losses, thereby affecting carbon sequestration potential. As an example, we suggest the implementation of the protocol at ten (10) permanent monitoring plots across three study areas located in Greece. The first group of plots represents a post-fire chronosequence in pine stands with no management interventions. The second group includes pine stands that exhibit variation in overstory and understory density driven by differences in microclimate and management history. The third group consists of peri-urban pine stands subjected to thinning of varying intensity. The monitoring protocol is implemented across all plots and the collected data can be classified into three analytical domains: (a) demography, encompassing measurements of tree growth and mortality; (b) litter and decomposition dynamics, involving the quantification of litterfall and its seasonality and the estimation of its decomposition rates; and (c) gas exchange, focusing on measurements of leaf photosynthesis and respiration (including relevant leaf functional traits) and monitoring of soil respiration. These three data domains can be used to comparatively consider the effect of forest management on key ecosystem processes and to constrain local-scale vegetation dynamics models. Full article

Monitoring soil CO₂ is essential for accurately quantifying the sources and sinks of atmospheric greenhouse gases and for providing carbon emission reduction strategies. However, the limited portability and high cost of conventional soil CO₂ monitoring equipment have severely restricted large-scale and long-term field observations. To address these constraints, this study has successfully designed and fabricated a portable and low-cost soil respiration system (SRS) based on non-dispersive infrared (NDIR) sensor technology and Long-range radio (LoRa) wireless communication. The SRS enables multi-point synchronous measurements and remote data transmission. Its reliability was rigorously evaluated through both simulated and field comparative experiments against the LI-8100A. The results demonstrated a high level of agreement between the measurements of the SRS and the LI-8100A, with the coefficients of determination (R²) of 0.996 and 0.997, respectively, for the simulation and field experiments, with the corresponding root mean square error (RMSE) of 0.090 and 0.089 μmol·m⁻²·s⁻¹. The Bland–Altman analysis further confirmed the consistency between the two systems, with over 95% of the data points falling within the acceptable limits of agreement. These findings indicate that the self-developed SRS substantially reduces costs while maintaining reliable measurement accuracy. With its wireless transmission and multi-point deployment capabilities, the SRS offered an efficient and practical solution for addressing the challenges of monitoring spatial heterogeneity of soil respiration, demonstrating considerable potential for broader application in CO₂ flux monitoring research. Full article

The adoption of conservation systems in agriculture has been increasingly explored as a strategy to improve soil quality and potentially influence greenhouse gas (GHG) emissions. This study reports the first assessment of GHG emissions within a long-term (14 years) agroecological field experiment evaluating soil management systems for onion (Allium cepa L.) production in a Humic Dystrudept (Cambissolo Húmico Distrófico, Brazilian Soil Classification System) in Southern Brazil. Three management systems based on permanent soil cover and crop diversification were evaluated in an onion–maize rotation: conventional tillage (CT) without cover crops, no-till (NT) without cover crops, and a no-till vegetable system (NTV) with a summer cover crop mixture of pearl millet (Pennisetum americanum), velvet bean (Mucuna aterrima), and sunflower (Helianthus annuus). Short-term GHG emissions were monitored during one onion growing season (106 days), while soil chemical and physical properties reflect long-term management effects. Evaluations included (i) daily and cumulative GHG (N₂O, CH₄, and CO₂) emissions, (ii) soil carbon (C) and nitrogen (N) stocks, (iii) soil aggregation, porosity, and bulk density in different soil layers (0.00–0.05, 0.05–0.10, and 0.10–0.30 m), and (iv) onion yield and cover crop dry matter production. The NTV system improved soil physical and chemical quality and increased onion yield compared to NT and CT. However, higher cumulative N₂O emissions were observed in NTV, highlighting a short-term trade-off between increased N₂O emissions and long-term improvements in soil quality and crop productivity. All systems acted as methane sinks, with greater CH₄ uptake under NTV. Despite higher short-term emissions, the NTV system maintained a positive C balance due to long-term C accumulation in soil. Short-term greenhouse gas emissions were assessed during a single onion growing season, whereas soil carbon stocks reflect long-term management effects; CO₂ fluxes measured using static chambers represent ecosystem respiration rather than net ecosystem carbon balance. These results provide an initial baseline of GHG dynamics within a long-term agroecological system and support future multi-year assessments aimed at refining mitigation strategies in diversified vegetable production systems. Full article

Nitrogen (N) deposition poses a multi-pronged threat to the carbon (C)-regulating services of moss understories. For forest C-cycle modeling under increasing N deposition, failure to mechanistically incorporate the moss-mediated processes risks severely overestimating the C sink potential of global forests. To explore whether and how N input affects the moss-mediated CH₄ and carbon dioxide (CO₂) fluxes, a five-year field measurement was performed in the N manipulation experimental plots treated with 22.5 and 45 kg N ha⁻¹ yr⁻¹ as ammonium chloride for nine years under a well-drained temperate forest in northeastern China. In the presence of mosses, the average annual CH₄ uptake and CO₂ emission in all N-treated plots ranged from 0.96 to 1.48 kg C-CH₄ ha⁻¹ yr⁻¹ and from 4.04 to 4.41 Mg C-CO₂ ha⁻¹ yr⁻¹, respectively, with a minimum in the high-N-treated plots, which were smaller than those in the control (1.29–1.83 kg C-CH₄ ha⁻¹ yr⁻¹ and 4.82–6.51 Mg C-CO₂ ha⁻¹ yr⁻¹). However, no significant differences in annual cumulative CO₂ and CH₄ fluxes across all treatments occurred without moss cover. Based on the differences in C fluxes with and without mosses, the average annual moss-mediated CH₄ uptake and CO₂ emission in the control were 0.77 kg C-CH₄ ha⁻¹ yr⁻¹ and 2.40 Mg C-CO₂ ha⁻¹ yr⁻¹, respectively, which were larger than those in the two N treatments. The N effects on annual moss-mediated C fluxes varied with annual meteorological conditions. Soil pH, available N and C contents, and microbial activity inferred from δ¹³C shifts in respired CO₂ were identified as the main driving factors controlling the moss-mediated CH₄ and CO₂ fluxes. The results highlighted that this inhibitory effect of increasing N deposition on moss-mediated C fluxes in the context of climate change should be reasonably taken into account in model studies to accurately predict C fluxes under well-drained forest ecosystems. Full article

In controlled environment agriculture (CEA), CO₂ enrichment can promote photosynthesis while simultaneously reducing evapotranspiration, but the optimal settings vary depending on crop type, growth stage, and microclimate. This study presents a near-field remote sensing framework that fuses RGB image features with environmental variables to predict the CO₂ uptake/respiration dynamics of five leafy vegetables grown in a hydroponic culture system and evaluate their impact on resource efficiency under CO₂ control. A hybrid deep model incorporating You Only Look Once version 11 (YOLOv11) and a Residual Network with 50 layers (ResNet50) extracts growth-related visual cues and integrates them with tabular features (CO₂, temperature, and light conditions) to predict chamber CO₂ dynamics. Performance was evaluated by Mean Absolute Error (MAE)/Mean Squared Error (MSE) on withheld data, and the system-level impacts on water use (ET), pumping energy, and relative yield were analyzed using a conventional greenhouse model. The model exhibited high accuracy (MAE = 0.95; MSE = 1.62). Scenario analysis results showed that increasing ambient CO₂ concentration from 400 to 1200 ppm reduced modeled water demand by approximately 11%, increased modeled yield by approximately 9%, and resulted in a corresponding reduction in pumping energy per unit area. Unlike conventional single-crop, table-based approaches, this study demonstrates multi-crop generalization and image-environment fusion for CO₂ dynamic prediction, establishing proximity sensing as a viable decision-making layer for CEA. While yield/ET results were simulated rather than measured in long-term trials, and leaf area normalization was not available, the proposed framework provides a viable path for data-driven CO₂ control in indoor farms by linking image-based monitoring with operational optimization. Full article

Maintaining an effective facial seal is critical for the performance of tight-fitting industrial respirators used in high-risk sectors such as mining, manufacturing, and construction. Traditional fit verification methods—Qualitative Fit Testing (QLFT) and Quantitative Fit Testing (QNFT)—are limited to periodic assessments and cannot detect fit degradation during active use. This study presents a real-time fit detection system based on embedded breath sensors and machine learning algorithms. A compact sensor module inside the respirator continuously measures pressure, temperature, and humidity, transmitting data via Bluetooth Low Energy (BLE) to a smartphone for on-device inference. This system functions as a multimodal biosensor: intra-mask pressure tracks flow-driven mechanical dynamics, while temperature and humidity capture the thermal–hygrometric signature of exhaled breath. Their cycle-synchronous patterns provide an indirect yet reliable readout of respirator–face sealing in real time. Data were collected from 20 healthy volunteers under fit and misfit conditions using OSHA-standardized procedures, generating over 10,000 labeled breathing cycles. Statistical features extracted from segmented signals were used to train Random Forest, Support Vector Machine (SVM), and XGBoost classifiers. Model development and validation were conducted using variable-size sliding windows depending on the person’s breathing cycles, k-fold cross-validation, and leave-one-subject-out (LOSO) evaluation. The best-performing models achieved F1 scores approaching or exceeding 95%. This approach enables continuous, non-invasive fit monitoring and real-time alerts during work shifts. Unlike conventional techniques, the system relies on internal physiological signals rather than external particle measurements, providing a scalable, cost-effective, and field-deployable solution to enhance occupational safety and regulatory compliance. Full article

Agricultural land management is a major determinant of terrestrial carbon (C) fluxes and has substantial implications for greenhouse gas (GHG) mitigation strategies. This study evaluated the net ecosystem carbon balance (NECB) of an agricultural field in an organic integrated crop–livestock system (ICLS) with a ley-arable rotation in northern Germany over two years (2021–2023). Carbon dioxide (CO₂) fluxes were measured using the eddy covariance (EC) method to derive net ecosystem exchange (NEE), gross primary production (GPP), and ecosystem respiration (RECO). This approach facilitated an assessment of the temporal dynamics of CO₂ exchange, alongside detailed monitoring of field-based C imports, exports, and management activities, of a crop sequence including grass-clover (GC) ley, spring wheat (SW), and a cover crop (CC). The GC ley acted as a consistent C sink (NECB: −1386 kg C ha⁻¹), driven by prolonged photosynthetic activity and moderate biomass removal. In contrast, the SW, despite high GPP, became a net source of C (NECB: 120 kg C ha⁻¹) due to substantial export via harvest. The CC contributed to C uptake during the winter period. However, cumulatively, it acted as a net CO₂ source, likely due to drought conditions following soil cultivation and CC sowing. Soil cultivation events contributed to short-term CO₂ pulses, with their magnitude modulated by soil water content (SWC) and soil temperature (TS). Overall, the site functioned as a net C sink, with an average NECB of −702 kg C ha⁻¹ yr⁻¹. This underscores the climate mitigation potential of management practices such as GC ley systems under moderate grazing, spring soil cultivation, and the application of organic fertilizers. To optimize CC benefits, their use should be combined with reduced soil disturbance during sowing or establishment as an understory. Additionally, C exports via harvests could be offset by retaining greater amounts of harvest residues onsite. Full article

Amphibian welfare, particularly pain assessment in anurans, remains understudied despite their ecological and biomedical significance. This study aimed to identify and validate operational pain indicators for adult anurans under professional care. A four-phase approach was used: a systematic literature review, expert validation with risk analysis, field validation in a zoological facility, and development of a preliminary pain index. From 158 publications, 16 potential indicators were identified, encompassing behavioural, clinical, and physiological signs. Expert evaluation by 28 professionals from 12 institutions refined this to seven indicators, achieving over 60% consensus: feeding behaviour changes, abnormal behaviour, impaired locomotion, oedema, reduced movement, retained skin post-moulting, and altered respiration. Field validation in 53 anurans confirmed high observability and ease of measurement, with feeding behaviour changes and oedema scoring highest for practicality (93.5% and 93.0%, respectively). These validated indicators provide a science-based foundation for routine welfare monitoring, enabling timely interventions. Their integration into husbandry protocols can enhance ethical standards, improve conservation outcomes, and increase public confidence in amphibian care, paving the way for a standardised anuran pain index. Full article

Intra-fractional respiratory management represents one of the greatest challenges of modern cancer radiotherapy (RT), as significant breathing-induced lesion motion might affect target coverage and organs at risk (OARs) sparing, jeopardizing oncological and toxicity outcomes. The detrimental effects on dosage of uncompensated organ motion are exacerbated in RT with charged particles (e.g., protons and carbon ions), due to their higher ballistic selectivity. The simplest strategies to counteract this phenomenon are the use of larger treatment margins and reductions in or control of respiration (e.g., by means of compression belts, breath hold). Gating and tracking, which synchronize beam delivery with the respiratory signal, also represent widely adopted solutions. When tracking the tumor itself or surrogates, invasive procedures (e.g., marker implantation), an unnecessary imaging dose (e.g., in X-ray-based fluoroscopy), or expensive equipment (e.g., magnetic resonance imaging, MRI) is usually required. When chest and abdomen excursions are measured to infer internal tumor displacement, the additional devices needed to perform this task, such as pressure sensors or surface cameras, present inherent limitations that can impair the procedure itself. In this context, radars have intrigued the radiation oncology community, being inexpensive, non-invasive, contactless, and insensitive to obstacles. Even if real-world clinical implementation is still lagging behind, there is a growing body of research unraveling the potential of these devices in this field. The purpose of this narrative review is to provide an overview of the studies that have delved into the potential of radar-based technologies for motion-adaptive photon and particle RT applications. Full article

Respiration rates in insects are critical for survival and environmental adaptation, being influenced by developmental stages, environmental conditions, and the regulation of mitochondrial protein-coding genes. However, methods for field-based measurements in small-sized insects remain limited. In this study, we established a portable photosynthesis system to quantify respiration rates in five small-sized insects (body length < 8 mm): Acyrthosiphon pisum, Aphis citricidus, Tuta absoluta, Tribolium castaneum, and Bactrocera dorsalis. We tested its effectiveness across life stages and under diverse treatments, including light/dark cycles, insecticides, temperature shifts, starvation, mitochondrial inhibitors, and RNA interference. The system exhibited high sensitivity and reproducibility rates, revealing stage-specific respiration patterns. Various treatments, as well as expression changes in mitochondrial protein-coding genes, significantly affected respiration rates. This study validates the portable system as a reliable tool for insect respiration studies and highlights regulatory networks associated with respiratory plasticity. These findings enhance experimental methodologies and advance our understanding of insect adaptation to environmental stressors and pest control strategies. Full article

In the context of Doppler radar, studies have examined the changes in the phase shift of the S₂₁ transmission coefficient related to minute movements of the human chest as a response to breathing or heartbeat. Detecting human vital signs remains a challenge, especially when obstacles interfere with the attempt to detect the presence of life. The sensitivity of a measurement system’s perception of vital signs is highly dependent on the monitoring systems and antennas that are used. The current work proposes a computational approach that aims to extract an empirical law of the dependence of the phase shift of the transmission coefficient (S₂₁) on the sensitivity at reception, based upon a set of four parameters. These variables are as follows: (a) the frequency of the continuous wave utilized; (b) the antenna type and its gain/directivity; (c) the electric field strength distribution on the chest surface (and its average value); and (d) the type of material (dielectric properties) impacted by the incident wave. The investigated frequency range is (1–20) GHz, while the simulations are generated using a doublet of dipole or gain-convenient identical Yagi antennas. The chest surface is represented by a planar rectangle that moves along a path of only 3 mm, with a step of 0.3 mm, mimicking respiration movement. The antenna–target system is modeled in the computational space in each new situation considered. The statistics illustrate the multiple regression function, empirically extracted. This enables the subsequent building of a continuous-wave bio-radar Doppler system with controlled and improved sensitivity. Full article

Biochar may represent a sustainable and eco-friendly strategy to recycle agroforestry wastes, sequester carbon and improve soil health. With the aim of proving these benefits in a real scenario, we treated several soil parcels with 0 (CTRL), 1 (LOW) and 3 (HIGH) kg/m² of wood biochar, in open-field trials. The heterotrophic soil respiration (SR) was monitored continuously for two months via a Closed Dynamic Chamber (CDC) associated with an innovative pilot system, and the most important soil chemical parameters were measured 9 and 54 days after biochar application. Biochar induced an immediate dose-dependent increase in organic matter content and CEC (up to 41.6% and 36.8% more than CTRL, respectively), which tended to slightly and gradually decrease after 54 days. In all cases, biochar induced a more pronounced SR, although the most enhanced microbial response was detected for the LOW parcel (19.3% higher than CTRL). Fennels were grown in treated soils and only LOW microplots gave a significantly better response (weight and size). Finally, NMR, FT-IR and Pyr-GC/MS analyses of LOW SOM extracts revealed a relevant impact on the composition, which was accompanied by a higher content of carbohydrates, indole-based compounds and FAME species correlating with enhanced microbial activity. Our findings demonstrate that the proper biochar dose improves soil fertility by creating an environment favorable to plants and promoting microbial activity. Full article

The apparent respiratory quotient (ARQ) of tree stems, defined as the ratio of net stem CO₂ efflux (E_{S_CO2}) to net stem O₂ influx (E_{S_O2}), offers insights into the balance between local respiratory CO₂ production and CO₂ transported via the xylem. Traditional static chamber methods for measuring ARQ can introduce artifacts and obscure natural diurnal variations. Here, we employed an open flow-through stem chamber with ambient air coupled with cavity ring-down spectrometry, which uses the molecular properties of CO₂ and O₂ molecules to continuously measure E_{S_CO2}, E_{S_O2}, and ARQ, at the base of a California cherry tree (Prunus ilicifolia) during the 2024 growing season. Measurements across three stem chambers over 3–11-day periods revealed strong correlations between E_{S_CO2} and E_{S_O2} and mean ARQ values ranging from 1.3 to 2.9, far exceeding previous reports. Two distinct diurnal ARQ patterns were observed: daytime suppression with nighttime recovery, and a morning peak followed by gradual decline. Partitioning E_{S_CO2} into local respiration and xylem-transported CO₂ indicated that the latter can dominate when ARQ exceeds 2.0. Furthermore, transported CO₂ exhibited a higher temperature sensitivity than local respiration, with both processes showing declining temperature sensitivity above 20 °C. These findings underscore the need to differentiate stem CO₂ flux components to improve our understanding of whole-tree carbon cycling. Full article

The measurement of tidal volumes via respiratory-induced surface movements of the upper body has been an objective in medical diagnostics for decades, but a real breakthrough has not yet been achieved. The improvement of measurement technology through new, improved sensor systems and the use of artificial intelligence have given this field of research a new dynamic in recent years and opened up new possibilities. Based on the measurement from a motion capture system, the respiration-induced surface motions of 16 test subjects were examined, and specific motion parameters were calculated. Subsequently, linear regression and a tailored convolutional neural network (CNN) were used to determine tidal volumes from an optimal set of motion parameters. The results showed that the linear regression approach, after individual calibration, could be used in clinical applications for 13/16 subjects (mean absolute error < 150 mL), while the CNN approach achieved this accuracy in 5/16 subjects. Here, the individual subject-specific calibration provides significant advantages for the linear regression approach compared to the CNN, which does not require calibration. A larger dataset may allow for greater confidence in the outcomes of the CNN approach. A CNN model trained on a larger dataset would improve performance and may enable clinical use. However, the database of 16 subjects only allows for low-risk use in home care or sports. The CNN approach can currently be used to monitor respiration in home care or competitive sports, while it has the potential to be used in clinical applications if based on a larger dataset that could be gradually built up. Thus, a CNN could provide tidal volumes, the missing parameter in vital signs monitoring, without calibration. Full article

Over time, the vegetation of abandoned rice paddy fields is succeeded by communities of willow (Salix pierotii Miq.). This study was carried out to confirm the potential for future carbon farming by evaluating the carbon absorption capacity of willow communities restored passively in abandoned rice paddy fields. The net primary productivity (NPP) of willow communities established in abandoned rice paddy fields in three areas of central Korea (Cheongju, Andong, and Buyeo) was determined. The NPP was obtained by combining the diameter growth of willow individuals and the density of willow stands, yielding 24.36, 19.74, and 38.69 tons·ha⁻¹·yr⁻¹, respectively, and the average NPP of the three sites was 27.62 tons·ha⁻¹·yr⁻¹. The carbon-based NPP calculated from the average NPP at the three sites was 13.81 tons·C·ha⁻¹·yr⁻¹, and the amount of heterotrophic respiration, which is the respiration of microorganisms and animals in the soil, measured in abandoned rice paddy fields in Cheongju was 5.25 tons·C·ha⁻¹·yr⁻¹. As a result, the net ecosystem production (NEP) of the willow communities established in the abandoned rice paddy fields was calculated as 8.56 tons·C·ha⁻¹·yr⁻¹. By substituting this NEP value into the area of abandoned rice paddy fields so far, the carbon dioxide absorption capacity of abandoned rice paddy fields was estimated to exceed 19 million·tons·yr⁻¹. This amount is high enough to account for 77% of the total forecasted carbon absorption capacity in 2050, which is the year Korea aims to achieve carbon neutrality. In this regard, carbon farming using abandoned rice paddy fields is evaluated as a promising project. Full article